IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2198-d773345.html
   My bibliography  Save this article

Energy Demand Analysis and Powertrain Design of a High-Speed Delivery Robot Using Synthetic Driving Cycles

Author

Listed:
  • Jari Vepsäläinen

    (Department of Mechanical Engineering, School of Engineering, Aalto University, Otakaari 4, 02150 Espoo, Finland)

Abstract

Last mile is known as the last leg of the delivery process, which is the most expensive and time-consuming part per kilometer. The most common last mile deliveries are postal packages, groceries and take away meals. Recently, there has been a growing interest and implementation of sidewalk autonomous delivery robots. These robots travel roughly at the speed of pedestrians (6 km/h). A high-speed (15 km/h) delivery robot design is proposed for reducing the time, energy and carbon footprint of deliveries. The preliminary design of the delivery robot powertrain is based on worst-case scenario analysis and Monte Carlo simulations with synthetic driving cycles. Synthetic driving cycles were used because there are no open data available of delivery robots. Thus, the procedure presented in this paper is a general approach for cases where there is no precedent application and/or no data are available. The synthetic driving cycles are based on start and end location of food-delivery services in Helsinki, Finland. Based on the simulations, the crucial factors contributing to energy demand and its variation were analyzed. Carbon footprint of the delivered package over distance of the design is compared to existing wheeled delivery robots and quadrupeds. The motivation of the work is that showcasing the energy savings of higher speed aids government officials in their decision-making regarding delivery robot regulations. As a result of the simulations, higher operation speed lowered the energy consumption by over 40%.

Suggested Citation

  • Jari Vepsäläinen, 2022. "Energy Demand Analysis and Powertrain Design of a High-Speed Delivery Robot Using Synthetic Driving Cycles," Energies, MDPI, vol. 15(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2198-:d:773345
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2198/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2198/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    2. Lei, Fei & Bai, Yingchun & Zhu, Wenhao & Liu, Jinhong, 2019. "A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort," Energy, Elsevier, vol. 167(C), pages 1040-1050.
    3. Ivan EVTIMOV & Rosen IVANOV & Gergana STANEVA & Georgi KADIKYANOV, 2015. "A study on electric bicycle energy efficiency," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 10(3), pages 131-140, September.
    4. Xu, Chonggang & Gertner, George Zdzislaw, 2008. "Uncertainty and sensitivity analysis for models with correlated parameters," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1563-1573.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Böhm & Mirco Nanni & Luca Pappalardo, 2022. "Gross polluters and vehicle emissions reduction," Nature Sustainability, Nature, vol. 5(8), pages 699-707, August.
    2. Lorenzo Barbieri & Roberto D’Autilia & Paola Marrone & Ilaria Montella, 2023. "Graph Representation of the 15-Minute City: A Comparison between Rome, London, and Paris," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    3. Zhiwei Bai & Hongkui Wei & Yingying Xiao & Shufang Song & Sergei Kucherenko, 2021. "A Vine Copula-Based Global Sensitivity Analysis Method for Structures with Multidimensional Dependent Variables," Mathematics, MDPI, vol. 9(19), pages 1-20, October.
    4. Miotti, Marco & Needell, Zachary A. & Jain, Rishee K., 2023. "The impact of urban form on daily mobility demand and energy use: Evidence from the United States," Applied Energy, Elsevier, vol. 339(C).
    5. Cao, Jiaokun & Du, Farong & Ding, Shuiting, 2013. "Global sensitivity analysis for dynamic systems with stochastic input processes," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 106-117.
    6. Shiqin Liu & Carl Higgs & Jonathan Arundel & Geoff Boeing & Nicholas Cerdera & David Moctezuma & Ester Cerin & Deepti Adlakha & Melanie Lowe & Billie Giles-Corti, 2021. "A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data," Papers 2105.08814, arXiv.org.
    7. Ospina, Juan P. & Duque, Juan C. & Botero-Fernández, Verónica & Montoya, Alejandro, 2022. "The maximal covering bicycle network design problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 222-236.
    8. Ning-Cong Xiao & Hong-Zhong Huang & Yan-Feng Li & Zhonglai Wang & Xiao-Ling Zhang, 2013. "Non-probabilistic reliability sensitivity analysis of the model of structural systems with interval variables whose state of dependence is determined by constraints," Journal of Risk and Reliability, , vol. 227(5), pages 491-498, October.
    9. Brinkley, Catherine & Raj, Subhashni, 2022. "Perfusion and urban thickness: The shape of cities," Land Use Policy, Elsevier, vol. 115(C).
    10. Jorge Ubirajara Pedreira Junior & Antônio Nélson Rodrigues da Silva & Cira Souza Pitombo, 2022. "Car-Free Day on a University Campus: Determinants of Participation and Potential Impacts on Sustainable Travel Behavior," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    11. Zhang, Bo & Zhang, Jiangyan & Shen, Tielong, 2022. "Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode," Applied Energy, Elsevier, vol. 305(C).
    12. Geoff Boeing, 2020. "Planarity and street network representation in urban form analysis," Environment and Planning B, , vol. 47(5), pages 855-869, June.
    13. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Brands, Ties & van Oort, Niels & Teller, David, 2021. "Multi-city exploration of built environment and transit mode use: Comparison of Melbourne, Amsterdam and Boston," Journal of Transport Geography, Elsevier, vol. 95(C).
    14. Alves d'Acampora, Bárbara Heliodora & Maraschin, Clarice & Taufemback, Cleiton Guollo, 2023. "Landscape ecology and urban spatial configuration: Exploring a methodological relationship. Application in Pelotas, Brazil," Ecological Modelling, Elsevier, vol. 486(C).
    15. Broto, Baptiste & Bachoc, François & Depecker, Marine & Martinez, Jean-Marc, 2019. "Sensitivity indices for independent groups of variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 163(C), pages 19-31.
    16. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.
    17. Ge, Qiao & Menendez, Monica, 2017. "Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 28-39.
    18. Bowater, David & Stefanakis, Emmanuel, 2023. "Extending the Adapted PageRank Algorithm centrality model for urban street networks using non-local random walks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    19. Boeing, Geoff & Pilgram, Clemens & Lu, Yougeng, 2024. "Urban Street Network Design and Transport-Related Greenhouse Gas Emissions around the World," SocArXiv r32vj, Center for Open Science.
    20. López-Benito, Alfredo & Bolado-Lavín, Ricardo, 2017. "A case study on global sensitivity analysis with dependent inputs: The natural gas transmission model," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 11-21.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2198-:d:773345. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.